เปรียบเทียบวิธี

	Regularized Logistic Regression	Naive Bayes
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1996–2005	1997
ผู้ริเริ่ม	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)	Mitchell, T. M. (textbook treatment)
ประเภท	Penalized classification model	Probabilistic classifier (Bayes' theorem with conditional independence)
แหล่งต้นตำรับ	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
ชื่อเรียกอื่น	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
ที่เกี่ยวข้อง	5	4
สรุป	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.